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Postdoctoral Position in Computational Materials Physics and Discovery at Los Alamos National Laboratory Los Alamos National Laboratory in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Postdoctoral position at Los Alamos National Laboratory; funding details, salary, and benefits are not specified in the post.
Country
United States
University
Los Alamos National Laboratory

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About this position
Los Alamos National Laboratory’s Quantum and Condensed Matter Physics Group in the Theoretical Division is hiring a postdoctoral researcher in computational materials physics.
The project centers on first-principles density functional theory (DFT), high-throughput materials discovery, and machine learning for the discovery and inverse design of novel 2D f-electron materials with non-trivial topological properties. The work includes developing computational workflows and applying existing DFT codes, with a strong connection to fundamental condensed matter theory and future quantum information applications.
The position is part of a collaborative effort involving scientists at Los Alamos National Laboratory and the University of California, Irvine. The work location is onsite in Los Alamos, New Mexico, United States.
Eligibility highlights: strong computational materials theory background, excellent publication record, experience with DFT methods such as VASP, Quantum Espresso, or Wien2k, and programming in Python, C++, or Fortran. Machine learning experience is highly desired. Strong communication skills and the ability to work independently and collaboratively are important. Experience with Linux and high-performance computing is preferred.
Application: review begins immediately and continues until the position is filled. Use the application portal linked in the post. Questions may be directed to Christopher Lane or Ying-Wai Li by email.
Funding details
Postdoctoral position at Los Alamos National Laboratory; funding details, salary, and benefits are not specified in the post.
What's required
Applicants must have strong skills in computational materials theory and an excellent scientific publication record. Required experience includes first-principles density functional theory simulations using standard DFT methods such as VASP, Quantum Espresso, or Wien2k, and programming experience in Python, C++, or Fortran. Knowledge of machine learning is highly desired but not required. Strong written and oral communication skills are essential, along with the ability to work creatively both independently and as part of a diverse team. Desired qualifications include experience with Linux and high-performance computing environments and basic machine learning knowledge.
How to apply
Apply through the application portal linked in the post. Review begins immediately and continues until the position is filled. For questions, contact Christopher Lane or Ying-Wai Li by email.
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